https://github.com/kargarisaac/macrpo
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Papers with Code 아카이브
Reinforcement Learning (RL)
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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/karim-abdel/fact
https://github.com/karishnu/tf-agents-multi-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/karthikncode/DeepRL-InformationExtraction
Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
https://github.com/karush17/emix
Maximum Mutation Reinforcement Learning for Scalable Control
https://github.com/karush17/esac
Maximum Mutation Reinforcement Learning for Scalable Control
https://github.com/kasnerz/tabgenie
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/katalinic/l2rl
https://github.com/katerakelly/pytorch-maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/kathyrnrouse/RL_CUIP
Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks
https://github.com/kavosh8/MAC
https://github.com/kayuksel/pytorch-ars
https://github.com/kayween/local-bo-mpd
https://github.com/kc-ml2/SimpleDreamer
https://github.com/kc-ml2/darts
https://github.com/kcorder/qmix_variants
https://github.com/kdexd/lang-emerge-parlai
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
https://github.com/ken011528/UpsideDown-reinforcement-learning-implement-by-tensorflow
https://github.com/kenfuliang/Awesome-Reinforcement-Learning
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
https://github.com/kentsommer/pytorch-value-iteration-networks
https://github.com/keraJLi/rejax
https://github.com/kered9/gym-microrts
Comparing Observation and Action Representations for Deep Reinforcement Learning in $μ$RTS
https://github.com/kevin-yu-0602/NeuralUCB_257
Provably Efficient Reinforcement Learning with Linear Function Approximation
https://github.com/kevin5naug/summer_project
https://github.com/kevinduh/san_mrc
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/kftam1994/Robo_Advisor
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/kfu02/jaxmarl
https://github.com/kgkolias/adversarially-motivated-intrinsic-goals-variation
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
https://github.com/kh-mo/QA_wikisql
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/khanrc/pt.darts
https://github.com/khashiii97/PNN
https://github.com/khashiii97/Progressive-Neural-Networks-for-IDS
https://github.com/kimbring2/DQFD_Minecraft
Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft
https://github.com/kimhc6028/pathnet-pytorch
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
https://github.com/kiminh/dual-policy-distillation
https://github.com/kinalmehta/epymarl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.